Intelligence

A clear explanation of how large language models work — from tokens and transformers to training and inference — without the hype.

RAG combines a language model with a search system to reduce hallucinations and give AI access to up-to-date information. Here is how it works.

AI agents are systems that use language models to plan and execute multi-step tasks. Here is a clear explanation of their architecture and limitations.

MCP is an open protocol for connecting AI assistants to tools and data sources. Here is what it does and why it matters.

How to write prompts that get reliable, useful outputs from large language models. Techniques backed by evidence, not folklore.

AI safety research addresses concrete technical problems about making AI systems behave reliably and in alignment with human intentions. Here is a clear overview.

How transformers work — self-attention, multi-head attention, positional encoding, and why the architecture dominates modern AI.

Fine-tuning adjusts a model's weights; prompting shapes its behavior at inference time. Here is a clear comparison of when each approach makes sense.

How to think about integrating AI into applications — choosing the right approach, handling failures, and designing for when the model gets it wrong.

Evaluating LLM-generated outputs is harder than evaluating deterministic systems. Here are the methods that work and the trade-offs between them.

The context window is one of the most important constraints in working with language models. Here is what it means in practice and how to work within it.

Modern AI systems process not just text but images, audio, and video. Here is how multimodal models work and what they enable.